Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement. This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes’ log2 foldchange (LFCs) that the drug candidates could induce. Among eight prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells. The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.


Introduction
Low prevalence and the rising numbers of rare diseases brings a substantial challenge for the study of disease etiology and the development of pharmaceutical interventions.Of the over 10,000 rare diseases affecting 30 million individuals in the US, only about 500 rare diseases have FDA-approved treatments (1).Glioblastoma (GBM), a rare type of highly aggressive brain cancer, is characterized by its devastatingly short survival time due to the absence of effective treatments.GBM is associated with an exceptionally high mortality rate, with roughly 30% of patients surviving only one year and less than 5% surviving ve years (2).This stark reality underscores the pressing demand for more effectively therapeutic options.Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear.Presently, the United States Food and Drug Administration (FDA) has approved four drugs for GBM,, none of which deliver satisfactory survival improvement, underscoring the imperative for innovative therapies (3).
Drug repurposing (DR), the discovery of existing drugs for new therapeutic use, emerges as a promising strategy for drug development (4,5).DR leverages the existing data on safety pro les, pharmacokinetics, and mechanisms of action of approved drugs, and thus can be a time and cost-effective alternative to traditional de novo drug development (6).By circumventing early-phase clinical trials and drug safety assessment, DR can signi cantly shorten the average development timeline from approximately 12 years to about 7 years (7).For instance, Hutchinson-Gilford progeria syndrome (HGPS) and Muckle-Wells syndrome (MWS) are two rare diseases with successful DR candidates, identi ed based on the pairing of cellular pathophysiology mechanisms and the drug's mechanism of action.Farnesyltransferase inhibitors (FTI), originally used for cancer treatment, showed therapeutic effect on HGPS, a rare premature aging disease, in which protein farnesylation plays a critical role, leading to the recent application for FDA approval as the rst ever treatment for HGPS (8).Canakinumab, a human IgG1 anti-IL-1β monoclonal antibody initially approved for rheumatoid arthritis, has been successfully repurposed for MWS, an autoin ammatory rare disorder caused by increased IL-1 (9).
With the current explosion of omics data reservoirs, which include genetics, transcriptomics, proteomics, and metabolomics datasets, computational method to uncover underlying biological mechanisms plays an increasing role in DR.Concurrently, substantial datasets concerning drugs' perturbation on gene expression of disease cell line models are increasingly available for use in DR (10), exempli ed by resources like the Connectivity Map (CMap) (11), LINCS (12), and iLINCS (13).Thus, linking drug responses and disease gene expression emerges as a promising strategy for DR.For example, via CMapbased transcriptome analysis, ivermectin has been identi ed as a new oncotherapy candidate for gastric cancer and its effect has been validated in wet-lab experiments (14).Furthermore, targeting these databases, gene expression signature-based screening approaches, such as reversal gene expression identi cation (15), have been proposed to identify DR candidates (16,17).For those feature genes that exhibit misregulation in a disease, a reversal gene expression is de ned when they were regulated in the opposite direction (upregulation vs. downregulation) in cell lines treated with a drug.
Although systematic approaches based on reversal gene expression have yielded promising DR candidates for cancers and several other common diseases (18), its application had not been reported for rare diseases.Therefore, in this study we adopted the aforementioned concept of reversal gene expression (15) to identify DR candidates for GBM by leveraging gene expression signature.Speci cally, we constructed a GBM gene expression pro le (GGEP) through an integrated differential gene expression analysis of transcriptome and proteome, aiming for an optimal characterization of GBM's mechanism.Targeting this GGEP we identi ed DR candidates with reversal gene expression signatures, the therapeutic effects of which were validated via cell viability assessment in GBM cell lines and control astrocytes.This omics-based DR approach illustrates the potential to signi cantly advance DR efforts in rare diseases and in non-rare diseases as well.

Methods and Materials
In this study, we attempted to integrate transcriptomics and proteomics for GBM gene expression pro le (GGEP) construction toward DR.The drug candidates identi ed with signi cant reversal gene expression were evaluated from multiple aspects to identify the top potential repurposing candidates.Figure 1 illustrates the study work ow comprising of four main components, candidate identi cation based on reversal gene expression (A, B and C), candidate prioritization based on reversal strength (D and E), candidate evaluation based on the identi ed scienti c evidence (F), and experimental evaluation (G and H).We describe each of the components in the following sections.
1. Drug candidate identi cation with reversal gene expressions to GBM.

GBM based multi-omics data preparation.
We collected transcriptome and proteome datasets from the Chinese Glioma Genome Atlas (CGGA) database (19) and an academic research paper (20) by following two criteria: 1) utilizing human brain tissue samples from GBM patients, and 2) conducting experiments on the same or similar platforms with analogous methodologies.
In this study, we utilized message RNA (mRNA) sequencing datasets collected from CGGA.Compared to total RNA transcriptomics, mRNA sequencing focuses on protein-coding genes which are translated into proteins.Proteomics data sets were derived from the experiment conducted by Buser et al. (20), which encompassed three GBM samples and three control samples.To the best of our knowledge, this experiment stands as the sole source of proteomics data that compares healthy control tissues and provides accessible original protein intensities.We downloaded the read counts for each gene from the mRNA sequencing and the signal intensities for each identi ed protein from proteomics experiments.
Principal component analysis (PCA) (21) was employed to estimate the similarity between each sample's gene expression pro les.Samples with signi cantly longer distances from their group clusters were considered outliers and were excluded from the dataset.The PCA was performed using the R package DEseq2 (22).

Deferential expression identi cation and GGEP construction.
We identi ed differentially expressed (DE) genes from both transcriptome and proteome datasets.A DE mRNA expression was identi ed as Benjamini-Hochberg (B-H) (23) adjusted p-value < 0.05 and absolute log 2 foldchange (|LFC|) >1.DE genes in the transcriptome datasets were determined via the standard procedure with the R package Deseq2.A DE protein translation was de ned as Bonferroni (24) adjusted p-value < 0.05 and |LFC| >1.DE proteins were identi ed from the proteome data set using the R stats package (25).As the LFC cannot be calculated for proteins that were detected only in one group, we manually set their fold changes as a xed value which approximates the maximum fold change detected in the experiment.Thereby we included these proteins with signi cant impacts on GBM.Based on the identi ed DE genes, we then constructed a GBM gene expression pro le (GGEP) comprising genes exhibiting both DE mRNA and DE protein expression in GBM.

Identi cation of drug candidates with reversal responses in the iLINCS database
We searched the iLINCS database (13) for drug responses that demonstrate reversal effect to GGEP.The iLINCS de nes a signature as the cell line's gene expression when perturbated by a particular chemical or drug.A signature was captured for each perturbation experiment.In this study we queried multiple signature libraries in iLINCS, including Cancer therapeutics response signatures (26), LINCS Chemical perturbagen signatures (LINCS L1000 assay) (12), Connectivity Map signatures (27), DrugMatrix signatures (28), Pharmacogenomics transcriptional signatures (29,30), and LINCS target proteomics signatures (31).The iLINCS auto-generated Pearson's correlation coe cient (i.e., the concordance), was used as an index for preliminary identi cation of reversal drug response signatures to GGEP.A negative concordance value indicates that the chemical-induced gene expression was inversely correlated with the GGEP (13).To include all potential candidates, we selected chemicals that induced gene expression signatures of a concordance score < -0.2 (32).Among these chosen chemicals, only FDA-approved drugs (33) (Published on June 6 th , 2023) were included for further analysis towards DR.

Drug candidate prioritization.
In the previous step, we identi ed drugs that could induce gene expression signatures that inversely correlated with the GGEP.In this step, we assessed the candidates' reversal strength via similarity clustering of their gene expression signatures and calculation of two self-de ned evaluation indices.In addition, we collected Blood-Brain Barrier (BBB) permeation probabilities of those candidates from the DrugBank database (34) to consider su cient drug uptake in the brain.

Candidates' gene expression signature clustering.
We retrieved gene expression signatures of the candidates from the iLINCS via its API (35), utilizing R packages knitr (36), tinytex (37), httr (38), jsonlite (39), htmltools (40), and Biobase (41).Subsequently, we clustered these signatures based on their expression features using the ComplexHeatmap R package (42).The matrix used for this clustering is DEG's LFC in each signature.The parameters used for the clustering are the Minkowski distances and Ward's hierarchical cluster method (43).Heatmap was employed to categorize the drugs' response signatures based on the similarity between their reversal gene expression and GGEP.

Regulation strength calculation.
To quantify the candidates' regulation strength, we de ned two indices, regulation score (RS) and overall coverage (OC) based on the number of genes in the GGEP they regulate and the LFCs of reversed gene expression they can produce respectively.

Regulation score (RS):
Based on the concept of Kullback-Leibler (KL) divergence (44), we introduced the RS which quanti es the regulation strength (i.e., LFC) based on the divergence between the GGEP and drug response signature (Formula 1).The RS is positively correlated with 1) the number of GBM-related genes it regulates, 2) the strength it regulates these GBM-related genes (LFC in the expression signature), and 3) the importance of the GBM-related genes it regulates (LFC in the GGEP).Thus, a potential drug candidate would be associated with a high RS, which illustrates its strong reverse effects on the expressions over GGEP genes.
The stand for the LFC of gene k in the gene expression feature of GBM and drug response signature, respectively.Theoretically, RS is a positive value ranges [0, +∞).The derivation and interpretation of RS can be found in the supplementary le 01 named "derivation and interpretation of regulation score.docx".

Overall coverage (OC)
We de ned an OC (formula 2) as the ratio of GBM-related genes regulated by drug candidates.OC is de ned as the percentage of the GGEP genes, whose gene expression could be reversed by a single drug.The OC was calculated following below formulas: In formulas (2), 'g' stands the number of the GBM-associated genes in the GGEP, while 'a' denotes the GBM-associated genes regulated by drugs (Figure 1. G). OC has positive values, ranged [0,1].A higher OC score indicates a higher ratio of GGEP genes that a treatment can reverse.

Drug candidate validation.
We evaluated the candidates with their possible mechanism of action in treating GBM in pre-clinical experiments and clinical trials via the Biomedical Data Translator (45) and the top ve candidates were further validated in in-vitro experiments.

Evaluation based on scienti c evidence.
We identi ed scienti c evidence to further evaluate and prioritize drug candidates.First, we examined if these drug candidates have undergone clinical trials for GBM treatment.We queried ClinicalTrial.gov using the keywords "glioblastoma", "high-grade glioma", and "GBM" in the "condition" eld to retrieve clinical trials in which the candidates have been used as intervention to treat GBM.In parallel, we also conducted literature search for candidates related clinical trials performed outside the US.Then, we explored their possible pharmacological mechanisms for GBM by collecting scienti c evidence from the NCATS Biomedical Data Translator (45).Speci cally, we utilized the ARAX reasoning engine (46) part of the Translator eco-system to identify any possible direct and indirect correlations between the candidates and GBM.In the end, we identi ed ve candidates with promising therapeutic effects that had not yet been investigated for clinical GBM use for further experimental evaluation.
All cell lines were cultured and maintained as recommended by the vendor.Seeding densities for each line were optimized in white, solid bottom 1536-well microplates (Greiner BioOne, Monroe, NC) in 6 µL of media per well.Cells were plated using the Multidrop Combi Liquid Dispenser (Thermo Fisher, Waltham, MA) at 200 cells/well except for U-87mg, T-98 G, U-118 MG, which were plated at 400, 150 and 300 cells/well, respectively.The plates were incubated at 37 °C with 5% CO 2 for six hours before adding compounds.Ten millimolar stock solution of Ciclopirox, Prochlorperazine, Clofarabine, Tacrolimus, and Tigecycline compounds were titrated in Dimethyl Sulphoxide (DMSO) at a 1:3 dilution in 384-well plates, which were then dispensed at 20 nL/well to 1536-well plates by Echo Acoustic Liquid Handling (Beckman Coulter, Inc., Brea, CA).Cells were incubated at 37 °C with 5% CO 2 with the compounds for 72 hours before adding 4.5 μl of CellTiter-Glo luminescent reagent (Promega, Madison, WI) per well.The plates were incubated at room temperature for 10 minutes before reading signal luminescence on PHERAStar plate reader (BMG Labtech, Cary, NC).Data was normalized to cells with 0.3% DMSO (100% viability) and 10 µM Staurosporine (0% viability) as a positive control.Concentration-response curves with corresponding relative half-maximal inhibitory concentration (IC 50 ) values were plotted and analyzed in GraphPad Prism 9 (GraphPad, Inc., San Diego).All results are shown as means of eight biological replicates ± standard deviation (SD).

Selectivity Assessment of Ciclopirox and Clofarabine.
We found Ciclopirox and Clofarabine exhibited the best IC50 curves in the above experiment, thus, we further evaluated their selectivity between GBM cells and astrocyte cells.Speci cally, iPSC-derived astrocytes (Fuji lm Cellular Dynamics, Cat#C1037) and all GBM lines were seeded in laminin-coated 35µL media at 2400 cells/well in 384-well plates for 24 hours at 37 °C with 5% CO 2 .Compounds were diluted in media before adding to the assay plate and further incubated for 72 hours at 37 °C with 5% CO 2 .Prior to reading luminescence, the bottom of the plate was sealed with white backing tape (after visualization of cells).A mixture of 35 μL/well of CellTiter-Glo luminescent reagent was added to the plates and the signal was read as described above.Results are shown as means of four or six replicates ± standard deviation (SD).

Cell viability staining.
GBM and astrocytes cell lines were plated in 1536 black clear bottom plates and treated with Ciclopirox and Clofarabine in parallel with plates for luminescence assays.After 72 hours of incubation, cells were xed with a nal concentration of 4% paraformaldehyde (PFA) for 20 minutes at room temperature.Cells were washed with Phosphate-buffered saline (PBS) followed by incubation with 0.5 µg/mL of highcontent screening CellMask green (Thermo Fisher Scienti c) and 4 µM Hoechst 33342 (Thermo Fisher Scienti c) at room temperature for 30 minutes.Cells were washed twice and sealed for imaging.
Imaging was performed on the Opera Phenix High Content Screening System (Revvity, Inc).

Results
1. Results on identifying drugs with reversal gene expression.
Adhering to our inclusion criteria described in the Methods, we obtained mRNA-seq data sets from three projects from the CGGA, containing 358 GBM patients and 20 healthy brain tissues.By performing the PCA, thirty outliers (supplemental Figure S1) were excluded from the subsequent DE analysis.We downloaded proteome datasets of three GBM samples and three control samples from Buser et al.'s study (20).GBM samples were extracted and pooled from eight GBM patients, while control samples were extracted and pooled from ve epileptic patients.There are no outliers identi ed in the proteome data sets thus all samples were included in the DE analysis (supplementary Figure S2).Combining these two sets resulted in 318 DE genes that exhibit signi cant regulation across both transcription and protein translation processes (Figure 2.A).Subsequently, we constructed the GGEP using the LFCs of these 318 genes transcription expression in GBM.The raw data and DEG analysis results of both omics were provided as supplementary le 03 named "MultiOmics_DEG_results.xlsx".In the GGEP, the top ten DE genes ranked by the LFC and adjusted p-value are associated with tumorigenesis (CDC45 (47,48), POSTN (49), KIF4A (50, 51), PEX5L (52), TFPI (53), GOLGA6L2 (54), NOL7 (55), GJB6 (56, 57), IGKV1-16 (58), and MOG ( 59)).For instance, CDC45 is associated with DNA methylation in a variety of cancers and its expression is negatively correlated with overall survival of GBM (48).POSTN, a matricellular protein implicated in gliomas and ovarian cancer, drives tumor growth and metastasis, in uences cell responses (49),and could serve as a potential biomarker for GBM survival prognosis (60).NOL7, positioned on chromosome 6p23, exhibits dual roles of suppressing cervical carcinoma cell growth while promoting melanoma progression (55).As shown in Figure 2.B, the DE genes in GGEP are enriched with cell proliferation-related GO terms and pathways (cell cycle, RNA metabolism, DNA metabolic processes, etc.) which re ect the excessive cell proliferation in tumor progression (61, 62).Notably, the enrichment of VEGFA-VEGFR2 signaling pathway, a major driver of tumor angiogenesis and metastasis indicates its prominent role in GBM mechanism.This pathway is instrumental in angiogenesis, fostering endothelial cell activities and vascular permeability, rendering it a promising target for therapy development across diverse cancers, including glioblastoma (63-65).

Results on identi cation of drugs with reversal gene expression
As shown in Table 3, 1,517 gene expression signatures were identi ed from iLINCS by applying the prede ned Concordance cutoff, calculated between the GGEP and the drug response signatures.These signatures were derived from perturbation experiments of 726 chemicals, which include 119 FDAapproved drugs.Detailed information of these signatures and chemicals can be found in the supplemental le 04 named "iLINCS result-signatures + chemicals.csv".
Twenty-one of these 119 drugs have undergone investigation in 215 GBM related clinical trials resulted by searching ClinicalTrials.gov.Temozolomide, as one of 21 drugs, is an FDA-approved treatment for GBM, has been studied in 169 clinical trials.The remaining 20 drugs have been investigated by an average of 2.3 trials.Dasatinib, Sirolimus, Hydroxyurea, and Etoposide, appeared in ve GBM based clinical trials individually.Notably, among the 21 drugs, there are three Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) inhibitors (Axitinib, Cabozantinib, and Dasatinib) and one EGFR inhibitor (Ge tinib).This observation proved the signi cance of the Vascular Endothelial Growth Factor A (VEGFA)-VEGFR2 signaling pathway in GBM progression, which was highlighted in the GGEP enrichment analysis (Figure 2.B), and thus targeting this pathway provides a promising research direction in the development of GBM treatment strategies.That being said, identi cation of these 21 drugs proved our methodology is valuable for DR, and remaining 98 drugs might be novel drug candidates for GBM to be examined.The detailed information of the 119 drugs can be found in the Supplementary File 05 named "drugs information (identi ed from iLINCs).csv".The Cutoff column lists the Concordance score value used to lter the signatures with reversal gene expressions.A negative concordance denotes a possible reversal gene expression to GGEP.After the rst-round screening using concordance <-0.2 as a cutoff, we further strain the cutoff to <-0.6 for the LINCS Chemical perturbagen library.This is based on the observation that much lower numbers of overlapped genes between its signatures and GGEP (approximately 10% of other signatures), which will increase false positive rate.The Signatures column lists the number of signatures identi ed in each signature library following the cutoffs.The Chemicals column lists the number of chemicals tested in these signatures.The Drugs column denotes the number of FDA-approved drugs identi ed accordingly.The row of Total denotes the numeric sum of signatures, chemicals, and drugs identi ed from all libraries, while the row of Unique lists the unique numbers of chemicals and drugs.
2. Results on drug candidate prioritization.

Gene expression signatures clustering results.
The 350 gene expression signatures of the 119 drugs were categorized into seven clusters with different reversal gene expression patterns, shown as cluster 1-6, and 8 in Figure 3.A (Cluster 7 was the LFC of GGEP in descending order).The cluster # in the heatmap visualized different reversal strengths of the clusters by comparing each gene's LFC in the drug's gene expression signatures to the GGEP.Among them, 24 drugs in three clusters (Clusters #1, #3, and #8) exhibited obvious reversal expressions targeting the GGEP.As illustrated in Figure 3.B, the GGEP gene expression could be reversed by the drugs in these three clusters.The expressions of the upregulated genes were reduced, and the downregulated genes were increased.It is noteworthy that the GGEP gene with higher LFCs were more strongly reversely regulated, indicating a high potential in reversing the GGEP.In contrast, the reversal effects of drugs in the rest of four clusters are either negligible or inaccessible due to a considerable number of missing values.Besides, cluster # 8 contains two signatures with a high ratio of missing values (gray column in heatmap), indicating that heatmap is not a reliable tool for candidate prioritization.The clustering results can be found in supplementary le 06 named "Clustering of iLINCS signatures.csv".
In addition, we plotted the heatmap at the drug level displaying each gene's median LFC of all gene expression signatures, the result con rmed the potential reversal effect of those 24 drugs (Figure S3).Seventeen of the 24 drugs have undergone clinical trials for GBM treatment, including Cabozantinib (66-68), Axitinib (NCT01508117, NCT01562197, NCT03291314), Mitomycin (NCT01580969, NCT02272270, and NCT02770378) (69), and Simvastatin (70).Twenty-two of these 24 drugs have a blood-brain barrier (BBB) penetration probability greater than 0.9, which indicates their possible drug delivery to GMB brain tissues.Table 4 lists information of these 24 drugs, including their BBB penetration probabilities, FDAapproved indications, and the number of GBM-related clinical trials they have been tested in.

Results on candidates' reversal strength assessment
Based on RS and OC, we evaluated the reversal effect on the candidates.Table 5 lists the top six individual candidates ranked by the calculated RS, which are consistent with their LFC (Figure 4 and Supplementary File 2).The calculated RS and OC and the bar plots for all candidates can be found in the Supplementary File 07 named "reversal_strength_indicies.csv"and Supplementary File 08 named "barplot w.RS_all candidates.pdf".
Among them, Romidepsin exhibits a signi cantly higher reversal effect than the others across all indices and from the direct expression of the bar plots.Romidepsin reverses the expression of 61% GGEP genes and its RS, which is a weighted sum of its reversal LFCs targeting these GGEP genes, is 25% higher than the other drugs.An example is Cabozantinib, although it can reverse more GGEP genes than Romidepsin (65.7% vs. 61%), its RS is lower due to smaller reversal LFCs it has.Noteworthy, the results of the signature clustering and the RS evaluation showed high consistency.Speci cally, there were 22 candidates (91.6%) presented in both the list of 24 candidates identi ed by the signature clustering and the list of top 24 candidates ranked by the RS.This suggests that the RS can be applied as an e cient indicator in selecting candidates with top reversal strengths.We identi ed ve top candidates based on the following criteria: 1) high RS score, 2) not tested in any clinical trials for GBM yet, and 3) high BBB penetration probability.The top ve candidates are Ciclopirox, Prochlorperazine, Clofarabine, Tacrolimus, and Tigecycline (Table 6).Some candidates with top RS were excluded because they have undergone clinical trials for GBM, such as, Romidepsin, Cabozantinib, Epirubicin Hydrochloride, and Axitinib, are associated with poor BBB penetration ability (71), or have failed a clinical trial when administered directly (72), like Docetaxel.We evaluated the potential mechanisms of action of these ve selected candidates for treating GBM based on scienti c evidence collected from the Biomedical Data Translator.
Ciclopirox, an inhibitor of metal-dependent enzymes, was used to treat onychomycosis of ngernails and toenails in immunocompetent patients (73).The result generated by the Translator is shown in Figure 6.Detailed evidence can be found in Supplementary File 9 or follow the link https://arax.ncats.io/?r=187830.Figure 6 showed that Ciclopirox might impact GBM mechanism via pathways associated with EGFR, VEGFA, TP53, and CXCR4.Subsequent literature review proved that Ciclopirox and bortezomib synergistically inhibit the growth of glioblastoma cell lines (U251, SF126, A172, and U118) via simultaneously enhancing JNK/p38 MAPK and NF-κB signaling (74).Another study showed that Ciclopirox inhibits the proliferation of cancer cell lines including MCF7 breast cancer cells, A549 lung cancer cells, and HT29 colon cancer cells) via suppressing Cdc25A (75).A recent study showed that Ciclopirox could inhibit U-251 GBM cell line via targeting deoxyhypusine hydroxylase (76).
Prochlorperazine is a dopamine D2 receptor antagonists used to treat schizophrenia and anxiety, as well as to relieve severe nausea and vomiting (77).The search results included in Supplementary File 10 (https://arax.ncats.io/?r=187832), from the Translator showed that Prochlorperazine might impact GBM mechanism of neoplastic cell transformation and tumor progression.One publication reported that Prochlorperazine induces concentration-dependent loss in the viability of human glioblastoma cells and its EC50 has been evaluated at the U87-MG cell line (78).
Clofarabine is a DNA polymerase inhibitor used to treat relapsed or refractory acute lymphoblastic leukemia (79).The evidence, generated by the Translator, is included in the Supplementary File 11 (https://arax.ncats.io/?r=233468) and shows that Clofarabine might impact GBM mechanism via pathways associated with STAT3, TP53, apoptosis, and neoplastic cell transformation.Currently Clofarabine is being tested as a repurposing drug to treat CLDN18.2+solid tumors (NCT05862324) and relapsed solid tumors (NCT02211755).However, its effect on GBM has not been reported yet.
Tacrolimus is an FDA-approved immunosuppressive agent used to prevent organ transplant rejection and to treat moderate to severe atopic dermatitis (80).The evidence generated by the Translator is included in the Supplementary File 12 (https://arax.ncats.io/?r=187831).It shows that Tacrolimus might impact GBM mechanism via pathways associated with EGFR, VEGFA, TP53, and apoptosis.The relevant publication proved that Tacrolimus attenuated the MRP1-mediated chemoresistant phenotype in GBM stem-like Cells (81).Tacrolimus could confer chemosensitivity to anticancer drugs in glioblastoma multiforme cells, offering a possible improvement to the current poor therapy available for high-grade human gliomas (82).
Tigecycline is a Glycylcycline antibiotic used to treat bacterial infections (83).The Translator results included in the Supplementary File 13 (https://arax.ncats.io/?r=187834) shows that Prochlorperazine might impact GBM tumor growth.Similar published results showed that Tigecycline inhibited glioma cell growth in an in vitro study by regulating the miRNA-199b-5p-HES1-AKT pathway (84).Besides, Tigecycline has demonstrated e cacy in restraining proliferation across various cancer types, including gastric cancer, melanoma, and neuroblastoma (85).

Therapeutic effects evaluation of top ve drug candidates.
Based on the systematic assessment of the drug candidates' reversal strength and evaluation of scienti c evidence regarding their mechanism of actions, we considered Ciclopirox, Prochlorperazine, Clofarabine, Tacrolimus, and Tigecycline as the most optimal candidates for in-vitro evaluationon GBM cell lines.
3.2.1.Concentration response assessment of top ve candidates on eight GBM cell lines.
For cell viability assay in each glioblastoma cell line, cell seeding density, choice and concentration of positive control, % incubation times were optimized for assay performance in 1536-well plates.Cells were incubated with 11 concentrations of each drug ranging from 0.56 nM to 33 µM.Data was normalized to cells treated with 0.3% DMSO as 100% viable cells and to 10 µM staurosporine as 0% viable cells.Based on these parameters, the calculated Z-factor of the assay for each cell line was between 0.65-0.82.The IC50 values and e cacy of drugs was determined by cell viability assays via a luminescent ATP content readouts.Out of the ve drugs tested, Clofarabine was the most e cacious in killing all glioblastoma cell lines with IC 50 values ranging from 36.9 nM to 467.5 nM (Figure 6.A).
Besides, Prochlorperazine, Tacrolimus, and Tigecycline demonstrated little to no effect on killing GBM cell lines (Figure 6. C, D, and E) 3.2.2.Selectivity and cell viability assessment of Ciclopirox and Clofarabine.
To assess the e cacy and speci city of Clofarabine and Ciclopirox on GBM cell lines, we then conducted a con rmation assay utilizing both the eight GBM cell lines and an astrocyte cell line as a noncancerous control.For consistency, all GBM lines and astrocytes were tested in 384-well plates under matching culture conditions.Data was normalized as described above, and the calculated Z-factor of this assay was 0.68.The IC 50 values for Clofarabine and Ciclopirox in astrocytes was 7.46 nM and 30.03 µM respectively (Figure 7.A and D).In comparison, the IC 50 values ranged from 177 nM to 1.06 µM for Clofarabine and 760 nM to 3.74 µM for Ciclopirox for the GBM cell lines (Figure 7.B and E).These data indicates that Clofarabine was more e cacious in killing GBM cells compared to astrocytes by a magnitude of 6-to 42-fold (Figure 7.C).In the case of Ciclopirox, GBM cells were 8-to 40-fold more susceptible than astrocytes to the drug (Figure 7.F).The results showed that both drugs had high speci city targeting GBM cell lines, their therapeutic effect on GBM warrants further investigation.
Figure 8 shows representative images of the difference in viabilities of two GBM cell lines and astrocytes when treated with 1.2 µM Clofarabine.At this concentration, Clofarabine at this concentration can kill GBM cells, while it has minimal effect on astrocytes.The staining images of all GBM and astrocyte cell lines treated with Clofarabine and Ciclopirox at 1.2 µM are provided in the supplementary le 14.The staining images at other concentrations are available upon request.

Discussion
The development of pharmaceutical interventions for rare diseases are challenged by low prevalence.Among them, GBM remains a devastating rare disease with limited treatment options and a short life expectancy.To ll the gap, in this study, we introduced a novel computational drug repurposing approach for GBM with consideration of the concept of reversal gene expression by performing multi-omics data analysis and in-vitro experiments.To this end, we successfully identi ed two promising drug candidates, Clofarabine and Ciclopirox for GBM, for further investigation.
In this study, we collected 328 transcriptome and 3 proteome data sets of GBM patients from a public database and a published study.Subsequently, we constructed the GGEP based on 318 DEGs resulting from multi-omics analysis.This GGEP proved to be an effective pro le in identifying DR candidates.However, the data type and sample size we used were limited due to the limited existing studies.When possible, the inclusion of more data types, such as whole genome sequencing data, metabolism data, and clinical data would produce deepened insight in GBM mechanisms and possibly more promising drug repurposing candidates.
We utilized two self-de ned indices, RS, and OC to quantify DR candidates' reversal strength.The results showed that RS can effectively prioritize candidates, resulting in promising candidates that were validated by in-vitro experiments.These two indices were calculated by comparing the averaged LFCs in drug expression signatures with those in GGEP.Inclusion of more features, such as drug concentrations and treatment time will improve the prioritization.Furthermore, these indices focus on individual drugs and cannot be directly applied on the prediction of combination therapies.The next step in our investigation is to expand the prioritization methods to re ect more aspects of the candidates' characteristics, such as toxicity, adverse effects, and drug-drug interactions.This will increase the robustness of the nal candidate selection, especially for the combination therapies.
For the candidates with the best RS scores, we evaluated the scienti c evidence collected by the Biomedical Data Translator and assessed their therapeutic effects on eight GBM cell lines.The collected evidence provides clues of pathways targeted by the drugs that could possibly impact GBM.The evidence was collected manually from one Translator web tool, this process will be more e ciently ful lled via the Translator Reasoner API (TRAPI) (86) when the study is expanded to more disease types.Through the in-vitro experiments, we identi ed Clofarabine and Ciclopirox as two promising repurposing drugs for GBM.These repurposing candidates will need further investigations including animal model e cacy evaluation and other preclinical studies to assess their potential for advancement to clinical trials.
Overall, this study introduced a novel computational approach that can effectively identify drug repurposing candidates for GBM.Clofarabine and Ciclopirox demonstrated high e cacy in inhibiting GBM cancer cells with selectivity against control astrocytes, and their potential for treating GBM is worth further investigation.At the time of this study, there was no existing multi-omics database designed speci cally for rare diseases, therefore we manually collected the omics data sets from various sources      Scienti c evidence collected by the Biomedical Data Translator.This network was constructed by possible interactions between Ciclopirox and GBM.We also include indirect interactions connected by another node, such as the VEGFA in this network.The green edges stand for high-con dence associations such as "regulates", "treats", "causes", or "associated with", while the blue edges stand for low-con dence associations, such as being discussed simultaneously in a study.Please note that direct edges between Ciclopirox and GBM do not always stand for existing studies that GBM has been treated by Ciclopirox.

Figure 2 Expression
Figure 2

Figure 4 Bar
Figure 4

Table 1 .
Basic Information of Transcriptomics Study Subjects 1.2.Results on DE gene analysis and GGEP construction.DE analysis of transcriptome datasets revealed 7,106 upregulated and 5,359 downregulated transcripts in GBM.DE analysis of proteome datasets identi ed 890 upregulated and 309 downregulated proteins in GBM.Table2shows calculated values for DE genes for both omics from raw data.

Table 2 .
Raw data format and DE analysis p-adj refers to adjusted p-values of the hypothesis test of mean gene expression level in GBM and control groups.Note: The Transcripts' LFC in this table were calculated after transformation and normalization of all genes' read counts using the R package DEseq2.

Table 3 .
Drugs identi ed in iLINCS with reversal gene expression signatures.

Table 4 .
Clinical characteristics for the identi ed DR candidatesBBBs are the Blood-Brain Barrier permeability probabilities obtained from the Drugbank database, and * indicates that the BBB were obtained from published studies as they were missing in the Drugbank database.The column of Clinical Trials lists the number of GBM related clinical trials registered in ClinicalTrials.gov,and # indicates that the clinical trials were identi ed via literature review.The column of Approved Indications lists the drugs' FDA-approved indications obtained from the Drugbank database.

Table 5 .
Reversal strength assessment results on the top candidates

Table 6 .
The selected ve top candidates